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The representation of groundwater dynamics in land surface models and their roles in global precipitation variations has received attention in recent years. Studies have revealed the overall higher soil moisture but rather diverse precipitation changes after incorporating the groundwater component in climate models. However, groundwater effects on large-scale atmospheric energy transport, the fundamental atmospheric variable regulating Earth’s climate, have not been explored thoroughly. In this study, a pair of idealized experiments corresponding to contrast globally fixed water table depths by AMIP-type simulations in the Community Earth System Model was conducted. In the wet (shallow water table) experiments, an increased meridional surface temperature gradient makes the mean meridional energy transports and Hadley circulation stronger than dry (deep water table) experiments over the tropics. Such energy transport changes are primarily attributed to the dynamic contribution (intensified Hadley circulation). The wet experiments make the simulated world be like an aquaplanet simulation with less land–sea temperature contrast and the enhancement (reduction) of mean meridional circulation (stationary eddies) energy transports. Furthermore, the South Asian monsoon circulation in the wet experiment shows a southward shift in the premonsoon season (April–June) and slight weakening in the mature phase (July and August). This study explores the impacts of the soil conditions caused by various water table depths on global energy transport and has further implications for climate model developments and experiment designs.

Anthropogenic land-use change, irrigation, is considered to strongly modulate the hydroclimate at the regional scale by directly triggering evaporative cooling as the preliminary local effect. However, subsequent interactions with the background climate are highly nonlinear, which introduces diverse and unexpected consequences. The North China Plain (NCP) is one of the regions where irrigation has expanded most rapidly since the 20th century. The scarce rainfall in this region makes it necessary for irrigation to supplement the level of soil water for agricultural production. In this study, we quantify the effect of irrigation on the regional climate in China. Two regional climate models, WRF and RegCM, are used to mimic the large-scale practice of irrigation on the NCP. The results of our experiments show consistent cooling and moistening effects centered over the NCP across all experiments. Although the moisture budget and wind field pattern demonstrate that the vertical downdraft and low-level divergence could inhibit rainfall, the humidification dominates the climatic response in the dry April-May-June and increases the amount of precipitation significantly and consistently in the NCP region and the surrounding area in northern China. The enhanced CAPE increase sharply on some ‘calm days’ when the vertical moisture advection is small, especially during afternoon, triggering frequent light rains convectively by destabilizing the atmosphere. The consistent response to irrigation in two different models that employ structurally different land surface schemes could enhance the robustness of the physical mechanism behind the precipitation increase in the heavily irrigated region of NCP.

The impact of land–atmosphere anomaly coupling on land variability is investigated using a new two-stage climate model experimental design called the “GLACE-Hydrology” experiment. First, as in the GLACE-CMIP5 experiment, twin sets of coupled land–atmosphere climate model (CAM5-CLM4.5) ensembles are performed, with each simulation using the same prescribed observed sea surface temperatures and radiative forcing for the years 1971–2014. In one set, land–atmosphere anomaly coupling is removed by prescribing soil moisture to follow the control model’s seasonally evolving soil moisture climatology (“land–atmosphere uncoupled”), enabling a contrast with the original control set (“land–atmosphere coupled”). Then, the atmospheric outputs from both sets of simulations are used to force land-only ensemble simulations, allowing investigation of the resulting soil moisture variability and memory under both the coupled and uncoupled scenarios. This study finds that in midlatitudes during boreal summer, land–atmosphere anomaly coupling significantly strengthens the relationship between soil moisture and evapotranspiration anomalies, both in amplitude and phase. This allows for decreased moisture exchange between the land surface and atmosphere, increasing soil moisture memory and often its variability as well. Additionally, land–atmosphere anomaly coupling impacts runoff variability, especially in wet and transition regions, and precipitation variability, although the latter has surprisingly localized impacts on soil moisture variability. As a result of these changes, there is an increase in the signal-to-noise ratio, and thereby the potential seasonal predictability, of SST-forced hydroclimate anomalies in many areas of the globe, especially in the midlatitudes. This predictability increase is greater for soil moisture than precipitation and has important implications for the prediction of drought.

Tropical deforestation can result in substantial changes in local surface energy and water budgets, and thus in atmospheric stability. These effects may in turn yield changes in precipitation. The Maritime Continent (MC) has undergone severe deforestation during the past few decades but it has received less attention than the deforestation in the Amazon and Congo rain forests. In this study, numerical deforestation experiments are conducted with global (i.e., Community Earth System Model) and regional climate models (i.e., Regional Climate Model version 4.6) to investigate precipitation responses to MC deforestation. The results show that the deforestation in the MC region leads to increases in both surface temperature and local precipitation. Atmospheric moisture budget analysis reveals that the enhanced precipitation is associated more with the dynamic component than with the thermodynamic component of the vertical moisture advection term. Further analyses on the vertical profile of moist static energy indicate that the atmospheric instability over the deforested areas is increased as a result of anomalous moistening at approximately 800–850 hPa and anomalous warming extending from the surface to 750 hPa. This instability favors ascending air motions, which enhance low-level moisture convergence. Moreover, the vertical motion increases associated with the MC deforestation are comparable to those generated by La Niña events. These findings offer not only mechanisms to explain the local climatic responses to MC deforestation but also insights into the possible reasons for disagreements among climate models in simulating the precipitation responses.

As the global atmosphere warms, water vapor concentrations increase with rising temperatures at a rate of 7%/K. Precipitation change is associated with increased moisture convergence, which can be decomposed into thermodynamic and dynamic contributions. Our previous studies involving Coupled Model Intercomparison Project Phase 3 (CMIP3) projections have suggested that seasonal disparity in changes of global precipitation is primarily associated with the thermodynamic contribution. In this study, a vertically integrated atmospheric water budget analysis using multiple reanalysis datasets demonstrated that dynamic changes played a significant role in seasonal precipitation changes during 1979–2008, especially in the global average and ocean average. The thermodynamic component exhibited almost consistent magnitude in the contribution of seasonal precipitation changes during 1979–2008 in both CMIP5_AMIP models and reanalysis datasets, whereas the dynamic component (related to the tendency of ω and water vapor climatology) made a lower or negative contribution in the CMIP5_AMIP models compared with the reanalysis datasets. Strengthened (weakened) ascending and descending motions in the reanalysis datasets (CMIP5_AMIP models), which were indicative of strengthened (weakened) seasonal mean circulation, tended to increase (reduce) precipitation in the wet season and reduce (increase) precipitation in the dry season during the study period. Vertical profiles of the tendency of moist static energy in the mid-to-upper troposphere suggested a trend toward stability in the CMIP5_AMIP models and one toward instability in the reanalysis datasets. Such disagreement in stability might be related to the different warming tendency in the mid-to-upper troposphere over the tropics.

The Amazon river basin receives ~2000 mm of precipitation annually and contributes ~17% of global river freshwater input to the oceans; its hydroclimatic variations can exert profound impacts on the marine ecosystem in the Amazon plume region (APR) and have potential far-reaching influences on hydroclimate over the tropical Atlantic. Here, we show that an amplified seasonal cycle of Amazonia precipitation, represented by the annual difference between maximum and minimum values, during the period 1979–2018, leads to enhanced seasonalities in both Amazon river discharge and APR ocean salinity. An atmospheric moisture budget analysis shows that these enhanced seasonal cycles are associated with similar amplifications in the atmospheric vertical and horizontal moisture advections. Hierarchical sensitivity experiments using global climate models quantify the relationships of these enhanced seasonalities. The results suggest that an intensified hydroclimatological cycle may develop in the Amazonia atmosphere-land-ocean coupled system, favouring more extreme terrestrial and marine conditions., The hydroclimatic variations of the Amazon River basin can exert profound impacts on the marine ecosystem in the Amazon plume region. Here the authors show that an amplified seasonal cycle of Amazonia precipitation during 1979–2018 leads to enhanced seasonality in both Amazon river discharge and ocean salinity.

Precipitation extremes are expected to become more frequent in the changing global climate, which may considerably affect the terrestrial hydrological cycle. In this study, Coupled Model Intercomparison Project Phase 5 archives have been examined to explore the changes in normalized terrestrial water fluxes (precipitation minus evapotranspiration minus total runoff, divided by the precipitation climatology) in three tropical rainforest areas: Maritime Continent, Congo, and Amazon. Results show that a higher frequency of intense precipitation events is predicted for the Maritime Continent in the future climate than in the present climate, but not for the Amazon or Congo rainforests. Nonlinear responses to extreme precipitation lead to a reduced groundwater recharge and a proportionately greater amount of direct runoff, particularly for the Maritime Continent, where both the amount and intensity of precipitation increase under global warming. We suggest that the nonlinear response is related to the existence of a higher near-surface soil moisture over the Maritime Continent than that over the Amazon and Congo rainforests. The wetter soil over the Maritime Continent also leads to an increased subsurface runoff. Thus, increased precipitation extremes and concomitantly reduced terrestrial water fluxes lead to an intensified hydrological cycle for the Maritime Continent. This has the potential to result in a strong temporal heterogeneity in soil water distribution affecting the ecosystem of the rainforest region and increasing the risk of flooding and/or landslides.

The water vapour content of the atmosphere has increased as a result of global warming, strengthening the hydrological cycle. An analysis of observational data suggests that wet seasons have become wetter, and dry seasons drier, in recent decades.

The annual range of precipitation, which is the difference between maximum and minimum precipitation within a year, is examined in climate model simulations under global warming. For global averages, the annual range of precipitation tends to increase as the globe warms. On a regional basis, this enhancement is found over most areas of the world, except for the bands along 30°S and 30°N. The enhancement in the annual range of precipitation is mainly associated with larger upward trends of maximum precipitation and smaller upward trends or downward trends of minimum precipitation. Based on the moisture budget analysis, the dominant mechanism is vertical moisture advection, both on a global average and on a regional scale. The vertical moisture advection, moisture convergence induced by vertical motion, includes the thermodynamic component, which is associated with increased water vapor, and the dynamic component, which is associated with changes in circulation. Generally, the thermodynamic component enhances the annual range of precipitation, while the dynamic component tends to reduce it. Evaporation has a positive contribution to both maximum and minimum precipitation, but very little to the annual range of precipitation. Even though evaporation and horizontal moisture advection are small for a global average, they could be important on a regional basis.

In this paper, a noise replacement scheme based on adaptive polynomial interpolation (NRAPI) is proposed, where the perfect noise detection is assumed. In the NRAPI, the zero-order and the first-order polynomial interpolation schemes are adaptively applied in the noise replacement according to the noise density. It is observed that the proposed zero-order noise replacement scheme generally has better contrast and visual quality when the noise density is high and that the proposed first-order noise replacement scheme has better visual quality for cases of low and mild noise density. Consequently, the proposed NRAPI combines the zero-order and the first-order noise replacement schemes for better performance. The proposed NRAPI is justified by several images and compared with two newly reported noise replacement schemes. The simulation results indicate that the proposed NRAPI has better visual quality of restored images than those from the zero-order and the first-order noise replacement schemes, as expected.

In this paper, the one-dimensional grey polynomial interpolation (GPI) presented in [10] is applied to impulse noise removal. The proposed approach is motivated by the following observations. First, a median based noise removal approach can be considered as a variation of neighborhood interpolation which is based on the zero-order polynomial. Second, it is well known that the performance of first-order polynomial interpolation is generally better than that with the zero-order. Therefore, a better quality of restored image can be generally expected when a first-order polynomial interpolation is applied in the noise removal. With the noise detection approach described in [15], the proposed GPI based noise removal approach is justified by four examples where comparisons are made with the median based approach reported in [11]. The results indicate that the proposed approach generally outperforms the compared approach.

In this paper, we apply the one-dimensional polynomial interpolation (1-D PI) to salt and pepper noise removal. The difference between the proposed approach and median based schemes is that the noise pixel is replaced by an interpolated value instead of median in the window. With 3 x 3 window, two non-noise neighbor pixels are used to estimate the noise pixel by first-order PI. Then the interpolated value takes the place of noise pixel. The proposed approach is justified by two examples. The results indicate that the proposed approach has better visual quality in the examples when compared with conventional median filter and adaptive median filter.

In a previous paper, we presented an image enlargement approach based on the grey polynomial interpolation (GPI) (Hsieh et al., 2007). In this paper, we propose an edge enhancement scheme to improve the enlarged image by the GPI. The proposed approach is called GPI/EE (GPI with edge enhancement). Basically, the edge enhancement consists of two stages. First, the edge in the enlarged image is enhanced by Laplacian filter. This doing relieves the blur in the enlarged image. Next, the zigzag edge in the image after Stage 1 is solved by smoothing neighborhood pixels around edges which are located by Canny edge detector. Examples are provided to justify the GPI/EE approach. Simulation results indicate that the enlargement image obtained by GPI/EE has better visual quality than that by GPI in the given examples.